Health model for cloud service health monitoring

Abstract

The techniques described herein automatically correlate the health of cloud resources to a broader health determination for an entity executing within, or supported by, a distributed computing environment. In contrast to the typical manual analysis that is required to make a broader health determination for a specific entity, the techniques generate and use a standard health model that can be applied, or scaled, to detect unhealthy scenarios across a variety of different entities with different owners (e.g., different tenants and/or different cloud resource providers). Furthermore, to meet varying owner perspectives on health, the techniques include a layer on top of the standard health model that enables an owner to provide input that customizes the standard health model for their own entity.

The invention claimed is: 
     
         1 . A method comprising:
 generating a directed graph health model that defines dependencies between nodes within a distributed computing environment, wherein:   the nodes include lower-level nodes representing lower-level entities;   the nodes include higher-level nodes representing higher-level entities; and   edges that connect respective pairs of nodes, the edges representing dependencies between the respective pairs of nodes;   for an individual lower-level entity of the lower-level entities:   monitoring, by the directed graph health model, values of a plurality of metrics that are collected in association with use of the individual lower-level entity; and   categorizing, by the directed graph health model, the individual lower-level entity as one of healthy or unhealthy by applying an anomaly detection algorithm to the values of the plurality of metrics;   determining, by the directed graph health model, a number of lower-level entities have been categorized as unhealthy, wherein each lower-level entity in the number of lower-level entities is connected to a higher-level entity in the directed graph health model via a respective edge;   determining, by the directed graph health model, that the number of lower-level entities satisfies a threshold established to indicate that a state of the higher-level entity is unhealthy, wherein:   the threshold defines a percentage of a total number of lower-level entities connected to the higher-level entity in the directed graph health model; and   the percentage and the total number of lower-level entities are specific to a type of lower-level entity;   in response to determining that the number of lower-level entities satisfies the threshold, accessing, by the directed graph health model, a rule associated with the higher-level entity, the rule defining an action to execute for the higher-level entity when the state of the higher-level entity is unhealthy; and   executing, by the directed graph health model, the action for the higher-level entity.   
     
     
         2 . The method of  claim 1 , wherein the action comprises one of:
 providing, to an owner of the higher-level entity, a notification indicating that the state of the higher-level entity is unhealthy;   transitioning the state of the higher-level entity from unhealthy to healthy by allocating cloud resources to the higher-level entity; or   transitioning the state of the higher-level entity from unhealthy to healthy by implementing a set of mitigation measures on the number of lower-level entities, wherein the set of mitigation measures is defined by the rule.   
     
     
         3 . The method of  claim 1 , wherein each lower-level entity of the lower-level entities and each higher-level entity of the higher-level entities comprises an identification parameter to distinguish one entity from a next entity. 
     
     
         4 . The method of  claim 3 , wherein the higher-level entities are different types of higher-level entities, the different types of higher-level entities comprising a tenant service type higher-level entity, a cloud resource provider service type higher-level entity, a geographic region type higher-level entity, a tenant type higher-level entity, or a cloud resource provider type higher-level entity. 
     
     
         5 . The method of  claim 4 , wherein the threshold established to indicate when the state of the higher-level entity is unhealthy is established based on a type of the higher-level entity. 
     
     
         6 . The method of  claim 3 , wherein the lower-level entities are of different types of lower-level entities, the different types of lower-level entities comprising a virtual machine type lower-level entity, a storage unit type lower-level entity, a container type lower-level entity, a physical server type lower-level entity, a network switch type lower-level entity, a container registry type lower-level entity, a key vault instance type lower-level entity, or a micro-service type lower-level entity. 
     
     
         7 . The method of  claim 3 , further comprising:
 causing the directed graph health model to be displayed on a display device along with a graphical indication that the state of the higher-level entity is unhealthy;   receiving a user selection of the higher-level entity via the directed graph health model caused to be displayed on the display device; and   based on the user selection, causing the identification parameters associated with the number of lower-level entities that have been categorized as unhealthy to be displayed.   
     
     
         8 . The method of  claim 7 , further comprising:
 receiving another user selection of another higher-level entity via the directed graph health model caused to be displayed on the display device, wherein the higher-level entity is connected to the other higher-level entity via a respective edge in the directed graph health model; and   causing an update to the identification parameters associated with the number of lower-level entities that have been categorized as unhealthy to be displayed, wherein the update reduces the number of lower-level entities that have been categorized as unhealthy to those that are connected to both the higher-level entity and the other higher-level entity in the directed graph health model.   
     
     
         9 . The method of  claim 1 , wherein the threshold is determined by a machine learning model configured to predict when a performance of the higher-level entity is degraded to a minimum threshold performance. 
     
     
         10 . The method of  claim 1 , wherein the higher-level entity, and the rule are defined based on input from a tenant of the distributed computing environment, wherein the tenant owns the higher-level entity. 
     
     
         11 . A system comprising:
 a processing system; and   a computer readable storage medium storing instructions that, when executed by the processing system, cause the system to perform operations comprising:   generating a directed graph health model that defines dependencies between nodes within a distributed computing environment, wherein:   the nodes include lower-level nodes representing lower-level entities;   the nodes include higher-level nodes representing higher-level entities; and   edges that connect respective pairs of nodes, the edges representing dependencies between the respective pairs of nodes;   for an individual lower-level entity of the lower-level entities:   monitoring, via the directed graph health model, values of a plurality of metrics that are collected in association with use of the individual lower-level entity; and   categorizing, via the directed graph health model, the individual lower-level entity as one of healthy or unhealthy by applying an anomaly detection algorithm to the values of the plurality of metrics;   determining, via the directed graph health model, a number of lower-level entities have been categorized as unhealthy, wherein each lower-level entity in the number of lower-level entities is connected to a higher-level entity in the directed graph health model via a respective edge;   determining, via the directed graph health model, that the number of lower-level entities satisfies a threshold established to indicate that a state of the higher-level entity is unhealthy, wherein:   the threshold defines a percentage of a total number of lower-level entities connected to the higher-level entity in the directed graph health mode; and   the threshold is determined by a machine learning model;   in response to determining that the number of lower-level entities satisfies the threshold, accessing, via the directed graph health model, a rule associated with the higher-level entity, the rule defining an action to execute for the higher-level entity when the state of the higher-level entity is unhealthy; and   executing, via the directed graph health model, the action for the higher-level entity.   
     
     
         12 . The system of  claim 11 , wherein the operations further comprise:
 causing the directed graph health model to be displayed on a display device along with a graphical indication that the state of the higher-level entity is unhealthy;   receiving a user selection of the higher-level entity via the directed graph health model caused to be displayed on the display device; and   based on the user selection, causing the identification parameters associated with the number of lower-level entities that have been categorized as unhealthy to be displayed.   
     
     
         13 . The system of  claim 11 , wherein the higher-level entity, and the rule are defined based on input from a tenant of the distributed computing environment, wherein the tenant owns the higher-level entity. 
     
     
         14 . A method comprising:
 generating a directed graph health model that defines dependencies between nodes within a distributed computing environment, wherein:   the nodes include lower-level nodes representing lower-level entities;   the nodes include higher-level nodes representing higher-level entities; and   edges that connect respective pairs of nodes, the edges representing dependencies between the respective pairs of nodes;   for an individual lower-level entity of the lower-level entities:   monitoring, by the directed graph health model, values of a plurality of metrics that are collected in association with use of the individual lower-level entity; and   categorizing, by the directed graph health model, the individual lower-level entity into one of a predefined set of health categories;   determining, by the directed graph health model, a number of lower-level entities have been categorized into a particular one of the predefined set of lower-level health categories, wherein each lower-level entity in the number of lower-level entities is connected to a higher-level entity in the directed graph health model via a respective edge;   determining, by the directed graph health model, that the number of lower-level entities satisfies a threshold established to indicate that a state of the higher-level entity has changed, wherein:   the threshold defines a percentage of a total number of lower-level entities connected to the higher-level entity in the directed graph health model; and   the percentage and the total number of lower-level entities are specific to a type of lower-level entity;   in response to determining that the number of lower-level entities satisfies the threshold, accessing, by the directed graph health model, a rule associated with the higher-level entity, the rule defining an action to execute for the higher-level entity when the state of the higher-level entity changes; and   executing, by the directed graph health model, the action for the higher-level entity.   
     
     
         15 . The method of  claim 14 , wherein:
 the predefined set of health categories includes healthy, unhealthy, and unknown; and   the state of the higher-level entity changes to one of a healthy state, an unhealthy state, or an unknown state.   
     
     
         16 . The method of  claim 15 , wherein the higher-level entity, and the rule are defined based on input from a tenant of the distributed computing environment, wherein the tenant owns the higher-level entity.